one noise variable, linear regression
## [1] "*************************************************************"
## [1] "one noise variable, linear regression"
## [1] "bSigmaBest 26"
## [1] "naive effects model"
## [1] "one noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2322 -0.6020 0.0120 0.5804 3.2574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001467 0.019623 0.075 0.94
## n1 1.000321 0.038697 25.850 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8776 on 1998 degrees of freedom
## Multiple R-squared: 0.2506, Adjusted R-squared: 0.2503
## F-statistic: 668.2 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.87711349635425"
## [1] " application rmse 1.15239485807949"
## [1] "one noise variable, linear regression naive effects model train rmse 0.87711349635425"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.136]
## [1] "one noise variable, linear regression naive effects model test rmse 1.15239485807949"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.285]
## [1] "effects model, sigma= 26"
## [1] "one noise variable, linear regression effects model, sigma= 26 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4199 -0.6771 -0.0035 0.6637 3.8867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.002211 0.022739 0.097 0.923
## n1 0.001391 0.003372 0.412 0.680
##
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared: 8.512e-05, Adjusted R-squared: -0.0004153
## F-statistic: 0.1701 on 1 and 1998 DF, p-value: 0.6801
##
## [1] " train rmse 1.01318496986114"
## [1] " application rmse 0.995905706944315"
## [1] "one noise variable, linear regression Laplace noised 26 train rmse 1.01318496986114"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.434]
## [1] "one noise variable, linear regression Laplace noised 26 test rmse 0.995905706944315"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.583]
## [1] "effects model, jacknifed"
## [1] "one noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4251 -0.6776 -0.0009 0.6645 3.8913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001465 0.022668 0.065 0.948
## n1 0.004279 0.038189 0.112 0.911
##
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared: 6.285e-06, Adjusted R-squared: -0.0004942
## F-statistic: 0.01256 on 1 and 1998 DF, p-value: 0.9108
##
## [1] " train rmse 1.01322491166252"
## [1] " application rmse 0.99567998170435"
## [1] "one noise variable, linear regression jackknifed train rmse 1.01322491166252"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.732]
## [1] "one noise variable, linear regression jackknifed test rmse 0.99567998170435"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.881]
## [1] "********"
## [1] "one noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9905 0.9968 0.9998 1.0010 1.0030 1.0210
## [1] 0.007566677
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.127 1.142 1.154 1.154 1.159 1.185
## [1] 0.01570842
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9905 0.9968 0.9994 1.0010 1.0040 1.0190
## [1] 0.007766595
## [1] "********"
## [1] "*************************************************************"
one variable, linear regression
## [1] "*************************************************************"
## [1] "one variable, linear regression"
## [1] "bSigmaBest 3"
## [1] "naive effects model"
## [1] "one variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3721 -0.6891 -0.0037 0.6848 3.7826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20623 0.02260 9.125 <2e-16 ***
## x1 1.00000 0.03685 27.137 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.2693, Adjusted R-squared: 0.269
## F-statistic: 736.4 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01025938596012"
## [1] " application rmse 0.999915402747535"
## [1] "one variable, linear regression naive effects model train rmse 1.01025938596012"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1308]
## [1] "one variable, linear regression naive effects model test rmse 0.999915402747535"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1457]
## [1] "effects model, sigma= 3"
## [1] "one variable, linear regression effects model, sigma= 3 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3804 -0.6915 -0.0012 0.6855 3.7891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20567 0.02261 9.098 <2e-16 ***
## x1 1.00679 0.03712 27.119 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.2691, Adjusted R-squared: 0.2687
## F-statistic: 735.5 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01043998850533"
## [1] " application rmse 1.00077015547304"
## [1] "one variable, linear regression Laplace noised 3 train rmse 1.01043998850533"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1606]
## [1] "one variable, linear regression Laplace noised 3 test rmse 1.00077015547304"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1755]
## [1] "effects model, jacknifed"
## [1] "one variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3933 -0.6946 -0.0039 0.6875 3.7985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2062 0.0227 9.084 <2e-16 ***
## x1 0.9871 0.0370 26.682 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.015 on 1998 degrees of freedom
## Multiple R-squared: 0.2627, Adjusted R-squared: 0.2623
## F-statistic: 712 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01481235978284"
## [1] " application rmse 1.00008428967326"
## [1] "one variable, linear regression jackknifed train rmse 1.01481235978284"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1904]
## [1] "one variable, linear regression jackknifed test rmse 1.00008428967326"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2053]
## [1] "********"
## [1] "one variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9887 0.9988 1.0020 1.0020 1.0050 1.0180
## [1] 0.006854633
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9887 0.9990 1.0020 1.0020 1.0050 1.0180
## [1] 0.006824758
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9887 0.9994 1.0030 1.0030 1.0050 1.0180
## [1] 0.007026908
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, linear regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, linear regression"
## [1] "bSigmaBest 17"
## [1] "naive effects model"
## [1] "one variable plus noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9216 -0.6181 0.0055 0.6225 3.5298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20622 0.02058 10.02 <2e-16 ***
## x1 0.83459 0.03452 24.17 <2e-16 ***
## n1 0.78131 0.03844 20.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9203 on 1997 degrees of freedom
## Multiple R-squared: 0.3946, Adjusted R-squared: 0.394
## F-statistic: 650.8 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.919591353886876"
## [1] " application rmse 1.12246743812363"
## [1] "one variable plus noise variable, linear regression naive effects model train rmse 0.919591353886876"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2480]
## [1] "one variable plus noise variable, linear regression naive effects model test rmse 1.12246743812363"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2629]
## [1] "effects model, sigma= 17"
## [1] "one variable plus noise variable, linear regression effects model, sigma= 17 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4407 -0.6842 0.0052 0.6823 3.7289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.216818 0.022652 9.572 <2e-16 ***
## x1 1.002290 0.037240 26.914 <2e-16 ***
## n1 0.006125 0.004126 1.484 0.138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.013 on 1997 degrees of freedom
## Multiple R-squared: 0.2668, Adjusted R-squared: 0.266
## F-statistic: 363.3 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01201210045781"
## [1] " application rmse 1.01234721515415"
## [1] "one variable plus noise variable, linear regression Laplace noised 17 train rmse 1.01201210045781"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2778]
## [1] "one variable plus noise variable, linear regression Laplace noised 17 test rmse 1.01234721515415"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2927]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3986 -0.6920 -0.0077 0.6877 3.8126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20643 0.02268 9.101 <2e-16 ***
## x1 0.98425 0.03698 26.614 <2e-16 ***
## n1 -0.07739 0.03479 -2.224 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 1997 degrees of freedom
## Multiple R-squared: 0.2645, Adjusted R-squared: 0.2638
## F-statistic: 359.2 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01355772650768"
## [1] " application rmse 1.00913108707443"
## [1] "one variable plus noise variable, linear regression jackknifed train rmse 1.01355772650768"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3076]
## [1] "one variable plus noise variable, linear regression jackknifed test rmse 1.00913108707443"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3225]
## [1] "********"
## [1] "one variable plus noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9862 0.9965 1.0050 1.0020 1.0090 1.0130
## [1] 0.008962906
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.091 1.119 1.127 1.130 1.142 1.167
## [1] 0.01843663
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9887 1.0010 1.0100 1.0080 1.0140 1.0200
## [1] 0.009739627
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, diagonal regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, diagonal regression"
## [1] "bSigmaBest 10"
## [1] "naive effects model"
## [1] "one variable plus noise variable, diagonal regression naive effects model fit model:"
## x1 n1
## 1.000005 1.000333
## [1] " train rmse 0.958540237968956"
## [1] " application rmse 1.20618715828122"
## [1] "one variable plus noise variable, diagonal regression naive effects model train rmse 0.958540237968956"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3652]
## [1] "one variable plus noise variable, diagonal regression naive effects model test rmse 1.20618715828122"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3801]
## [1] "effects model, sigma= 10"
## [1] "one variable plus noise variable, diagonal regression effects model, sigma= 10 fit model:"
## x1 n1
## 1.00058530 0.01889345
## [1] " train rmse 1.03170258962911"
## [1] " application rmse 1.03576497717631"
## [1] "one variable plus noise variable, diagonal regression Laplace noised 10 train rmse 1.03170258962911"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3950]
## [1] "one variable plus noise variable, diagonal regression Laplace noised 10 test rmse 1.03576497717631"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4099]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, diagonal regression effects model, jackknifed fit model:"
## x1 n1
## 0.9871528 -0.1088369
## [1] " train rmse 1.03458802692346"
## [1] " application rmse 1.03176880530955"
## [1] "one variable plus noise variable, diagonal regression jackknifed train rmse 1.03458802692346"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4248]
## [1] "one variable plus noise variable, diagonal regression jackknifed test rmse 1.03176880530955"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4397]
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.004 1.012 1.018 1.019 1.024 1.033
## [1] 0.008702826
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.179 1.205 1.217 1.217 1.227 1.248
## [1] 0.01815138
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.008 1.019 1.029 1.027 1.032 1.043
## [1] 0.01027036
## [1] "********"
## [1] "*************************************************************"